Machine Learning Algorithms for Failure Prediction Model and Operational Reliability of Onshore Gas Transmission Pipelines
A transmission pipeline is the safest and most effective way of transporting large volumes of natural gas over long distances. However, if not maintained efficiently, failures of gas transmission pipelines can occur and cause catastrophic events. Therefore, an accurate prediction of pipe failure...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Indonesia
2023-05-01
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| Series: | International Journal of Technology |
| Subjects: | |
| Online Access: | https://ijtech.eng.ui.ac.id/article/view/6287 |
| Summary: | A
transmission pipeline is the safest and most effective way of transporting
large volumes of natural gas over long distances. However, if not maintained
efficiently, failures of gas transmission pipelines can occur and cause
catastrophic events. Therefore, an accurate prediction of pipe failures and
operational reliability is required to determine the optimal pipe replacement timing
such that the incidence of pipe failures can be prevented. Nowadays,
computer-assisted technology helps businesses make better decisions, and
machine learning is among the excellent techniques that can be utilized in
predicting failures. In this study, two machine learning algorithms, i.e.,
random forest and binary logistic regression, are developed, and their
prediction abilities are compared. The model is developed based on a decade of
unstructured and complex historical failure data of the onshore gas
transmission pipelines released by the United States Department of
Transportation. The modeling process begins with data pre-processing followed
by model training, model testing, performance measuring, and failure
predicting. Both algorithms have demonstrated excellent
results. The random forest model achieved an AUC of 0.89 and a predictive
accuracy of 0.913, while the binary logistic regression model outperformed with
an AUC of 0.94 and a prediction accuracy of 0.949. The trained model is further
employed to predict future failures on a 11900-mile natural gas pipeline
spanning from Louisiana to the northeast section of the United States. We show
the location of the pipes that will be broken in the interval of five years and
estimate that 29%/63%/83% of the pipes will break by 2025/2030/2035. |
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| ISSN: | 2086-9614 2087-2100 |